Example 3: Simplest Case - with a Priority Resource

from examples.example_3_simplest_case_priority_resource.ex_3_model_classes import Trial, g
from vidigi.prep import reshape_for_animations, generate_animation_df
from vidigi.animation import generate_animation
from vidigi.animation import animate_activity_log
import pandas as pd
import plotly.io as pio
pio.renderers.default = "notebook"
my_trial = Trial()

my_trial.run_trial()
4 nurses

my_trial.all_event_logs.head(50)
patient pathway event_type event time resource_id run
0 1 2 arrival_departure arrival 0.000000 NaN 0
1 1 2 queue treatment_wait_begins 0.000000 NaN 0
2 1 2 resource_use treatment_begins 0.000000 1.0 0
3 2 2 arrival_departure arrival 3.399660 NaN 0
4 2 2 queue treatment_wait_begins 3.399660 NaN 0
5 2 2 resource_use treatment_begins 3.399660 2.0 0
6 3 2 arrival_departure arrival 8.497645 NaN 0
7 3 2 queue treatment_wait_begins 8.497645 NaN 0
8 3 2 resource_use treatment_begins 8.497645 3.0 0
9 4 2 arrival_departure arrival 8.596678 NaN 0
10 4 2 queue treatment_wait_begins 8.596678 NaN 0
11 4 2 resource_use treatment_begins 8.596678 4.0 0
12 5 2 arrival_departure arrival 8.608025 NaN 0
13 5 2 queue treatment_wait_begins 8.608025 NaN 0
14 6 2 arrival_departure arrival 11.359739 NaN 0
15 6 2 queue treatment_wait_begins 11.359739 NaN 0
16 7 2 arrival_departure arrival 19.509442 NaN 0
17 7 2 queue treatment_wait_begins 19.509442 NaN 0
18 8 2 arrival_departure arrival 22.877356 NaN 0
19 8 2 queue treatment_wait_begins 22.877356 NaN 0
20 9 2 arrival_departure arrival 26.653863 NaN 0
21 9 2 queue treatment_wait_begins 26.653863 NaN 0
22 1 2 resource_use_end treatment_complete 40.317385 1.0 0
23 1 2 arrival_departure depart 40.317385 NaN 0
24 5 2 resource_use treatment_begins 40.317385 1.0 0
25 10 2 arrival_departure arrival 40.737793 NaN 0
26 10 2 queue treatment_wait_begins 40.737793 NaN 0
27 2 2 resource_use_end treatment_complete 42.443230 2.0 0
28 2 2 arrival_departure depart 42.443230 NaN 0
29 6 2 resource_use treatment_begins 42.443230 2.0 0
30 4 2 resource_use_end treatment_complete 48.809628 4.0 0
31 4 2 arrival_departure depart 48.809628 NaN 0
32 7 2 resource_use treatment_begins 48.809628 4.0 0
33 3 2 resource_use_end treatment_complete 51.483457 3.0 0
34 3 2 arrival_departure depart 51.483457 NaN 0
35 8 2 resource_use treatment_begins 51.483457 3.0 0
36 11 2 arrival_departure arrival 71.026558 NaN 0
37 11 2 queue treatment_wait_begins 71.026558 NaN 0
38 5 2 resource_use_end treatment_complete 77.447488 1.0 0
39 5 2 arrival_departure depart 77.447488 NaN 0
40 9 2 resource_use treatment_begins 77.447488 1.0 0
41 6 2 resource_use_end treatment_complete 83.962251 2.0 0
42 6 2 arrival_departure depart 83.962251 NaN 0
43 10 2 resource_use treatment_begins 83.962251 2.0 0
44 12 2 arrival_departure arrival 87.458700 NaN 0
45 12 2 queue treatment_wait_begins 87.458700 NaN 0
46 13 2 arrival_departure arrival 87.465138 NaN 0
47 13 2 queue treatment_wait_begins 87.465138 NaN 0
48 7 2 resource_use_end treatment_complete 95.498040 4.0 0
49 7 2 arrival_departure depart 95.498040 NaN 0 
STEP_SNAPSHOT_MAX = 45
LIMIT_DURATION = g.sim_duration
WRAP_QUEUES_AT = 15
full_patient_df = reshape_for_animations(
    event_log=my_trial.all_event_logs[my_trial.all_event_logs['run']==1],
    every_x_time_units=2,
    step_snapshot_max=STEP_SNAPSHOT_MAX,
    limit_duration=LIMIT_DURATION,
    debug_mode=True
    )

full_patient_df.head(15)
Iteration through minute-by-minute logs complete 18:07:18
Snapshot df concatenation complete at 18:07:19
index patient pathway event_type event time resource_id run rank minute additional
0 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 0 NaN
1 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 2 NaN
2 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 4 NaN
3 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 6 NaN
4 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 8 NaN
5 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 10 NaN
6 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 12 NaN
7 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 14 NaN
8 5 2 2 resource_use treatment_begins 12.021043 2.0 1 2.0 14 NaN
9 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 16 NaN
10 5 2 2 resource_use treatment_begins 12.021043 2.0 1 2.0 16 NaN
11 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 18 NaN
12 5 2 2 resource_use treatment_begins 12.021043 2.0 1 2.0 18 NaN
13 2 1 1 resource_use treatment_begins 0.000000 1.0 1 1.0 20 NaN
14 5 2 2 resource_use treatment_begins 12.021043 2.0 1 2.0 20 NaN 
event_position_df = pd.DataFrame([
                    {'event': 'arrival',
                     'x':  50, 'y': 300,
                     'label': "Arrival" },

                    # Triage - minor and trauma
                    {'event': 'treatment_wait_begins',
                     'x':  205, 'y': 275,
                     'label': "Waiting for Treatment"},

                    {'event': 'treatment_begins',
                     'x':  205, 'y': 175,
                     'resource':'n_cubicles',
                     'label': "Being Treated"},

                    {'event': 'exit',
                     'x':  270, 'y': 70,
                     'label': "Exit"}

                ])

Generate animation using the step-by-step functions

Using the three step-by-step functions allows us to intervene in the produced dataframe and manually take control of the icons in use.

This will allow us to show the high-priority patients with a unique icon so we can see their frequency and how they are handled in the final model.

full_patient_df_plus_pos = generate_animation_df(full_patient_df=full_patient_df,
                                                 event_position_df=event_position_df,
                                                 wrap_queues_at=WRAP_QUEUES_AT,
                                                 step_snapshot_max=STEP_SNAPSHOT_MAX,
                                                 gap_between_entities=10,
                                                 gap_between_resources=10,
                                                 gap_between_rows=30,
                                                 debug_mode=True
                                                 )

full_patient_df_plus_pos.sort_values(['patient', 'minute']).head(15)
Placement dataframe finished construction at 18:07:19
index patient pathway event_type event time resource_id run rank minute additional x y_final label resource x_final row icon
10186 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 0 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10187 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 2 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10188 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 4 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10189 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 6 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10190 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 8 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10191 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 10 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10192 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 12 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10193 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 14 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10194 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 16 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10195 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 18 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10196 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 20 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10197 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 22 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10198 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 24 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10199 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 26 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼
10200 2 1 1 resource_use treatment_begins 0.0 1.0 1 1.0 28 NaN 205 175.0 Being Treated n_cubicles 205.0 0.0 🧔🏼 
def show_priority_icon(row):
            if "more" not in row["icon"]:
                if row["pathway"] == 1:
                        return "🚨"
                else:
                    return row["icon"]
            else:
                return row["icon"]
full_patient_df_plus_pos = full_patient_df_plus_pos.assign(
            icon=full_patient_df_plus_pos.apply(show_priority_icon, axis=1)
            )
full_patient_df_plus_pos.head(15)
index patient pathway event_type event time resource_id run rank minute additional x y_final label resource x_final row icon
0 155 49 2 queue exit 206.540636 NaN 1 3.0 600 NaN 270 70.0 Exit NaN 250.0 0.0 🧕🏾
1 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 208 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
2 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 210 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
3 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 212 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
4 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 214 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
5 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 216 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
6 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 218 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
7 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 220 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
8 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 222 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
9 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 224 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
10 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 226 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
11 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 228 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
12 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 230 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
13 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 27.0 232 NaN 205 305.0 Waiting for Treatment NaN 95.0 1.0 🧕🏾
14 155 49 2 queue treatment_wait_begins 206.540636 NaN 1 26.0 234 NaN 205 305.0 Waiting for Treatment NaN 105.0 1.0 🧕🏾 
generate_animation(
        full_patient_df_plus_pos=full_patient_df_plus_pos.sort_values(['patient', 'minute']),
        event_position_df= event_position_df,
        scenario=g(),
        debug_mode=True,
        setup_mode=False,
        include_play_button=True,
        icon_and_text_size=20,
        plotly_height=700,
        frame_duration=800,
        frame_transition_duration=200,
        plotly_width=1200,
        override_x_max=300,
        override_y_max=500,
        time_display_units="dhm",
        display_stage_labels=False,
        add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_1_simplest_case/Simplest%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
    )
Output animation generation complete at 18:07:20

Rerun, but using the all-in-one animation function (which will not show different priority icons)

animate_activity_log(
        event_log=my_trial.all_event_logs[my_trial.all_event_logs['run']==1],
        event_position_df= event_position_df,
        scenario=g(),
        debug_mode=True,
        setup_mode=False,
        every_x_time_units=1,
        include_play_button=True,
        icon_and_text_size=20,
        gap_between_entities=6,
        gap_between_rows=25,
        plotly_height=700,
        frame_duration=200,
        plotly_width=1200,
        override_x_max=300,
        override_y_max=500,
        limit_duration=g.sim_duration,
        wrap_queues_at=25,
        step_snapshot_max=125,
        time_display_units="dhm",
        display_stage_labels=False,
        add_background_image="https://raw.githubusercontent.com/Bergam0t/vidigi/refs/heads/main/examples/example_1_simplest_case/Simplest%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png",
    )
Animation function called at 18:07:20
Iteration through minute-by-minute logs complete 18:07:22
Snapshot df concatenation complete at 18:07:22
Reshaped animation dataframe finished construction at 18:07:22
Placement dataframe finished construction at 18:07:22
Output animation generation complete at 18:07:24
Total Time Elapsed: 4.32 seconds